Time collection forecasting (TSF) is an essential predictive modeling device for plenty of choice-making contexts, particularly those regarding economic change control. Time collection forecasting involves analyzing a...
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ISBN:
(纸本)9798350370249
Time collection forecasting (TSF) is an essential predictive modeling device for plenty of choice-making contexts, particularly those regarding economic change control. Time collection forecasting involves analyzing a given variable's past observations or ancient information to expect destiny values or traits in the equal variable. It's far more and more being used in monetary markets to higher apprehend and expect price moves and different monetary trends, as well as within the monetary chance management context to detect rising dangers and assume destiny marketplace moves. There is an expansion of tactics used in TSF, starting from simple linear models to more significant advanced machine-gaining knowledge of strategies. In this paper, we offer a top-level view of the software of TSF for monetary danger management, with particular emphasis on methods for assessing monetary market dangers and strategies for predicting stock fee moves. We describe the processes available to economic practitioners, including the most famous ones, such as autoregressive shifting standard models, synthetic neural networks, and guide vector machines. ultimately, we speak of the challenges associated with applying TSF in the monetary chance control context and touch upon the modern-day and destiny traits within the region. Time series forecasting is a critical device for financial hazard management. It is a statistical device used to investigate and expect future values of monetary time collection, along with stock expenses, trade charges, interest prices, commodity expenses, stock market indices, etc. Time series forecasting uses quantitative strategies, linear regression, autoregression, and moving averages. It can also rent extra complex strategies, including autoregressive transferring averages and neural networks, to extract and interpret styles in the underlying records. The principle cause of this approach is to offer measures of economic danger and go back, which can be utilized
Our multimedia information retrieval Agent handles dynamic online material using an inventive method. It gathers processes, and stores current information in a variety of forms, including text, images, and video, usin...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Our multimedia information retrieval Agent handles dynamic online material using an inventive method. It gathers processes, and stores current information in a variety of forms, including text, images, and video, using text queries given by the user. By using sophisticated web scraping methods with Google as the main source, user interests are accurately represented. The main feature of the system is its ability to use MongoDB to smoothly integrate several multimedia formats into a single storage structure. Google changes their data on a scheduled basis to ensure accuracy and relevancy. With its user-friendly interface for natural language searches and visually appealing results presentation, the system effectively arranges and retrieves data. Robust authorization and authentication processes guarantee the emphasis on security, privacy, and ethical use. With an emphasis on scalability, dependability, and ongoing improvement, the design accounts for potential expansion while guaranteeing that it will always be a state-of-the-art and trustworthy multimedia retrieval technique.
Online Social networking Sites (OSNS) are a very fast-growing field in the present world. Large data is generated by the various types of users. It is very important to every user to provide security and privacy for t...
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The worlds of computing, communication, and storage have for a long time been treated separately, and even the recent trends of cloud computing, distributed computing, and mobile edge computing have not fundamentally ...
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ISBN:
(纸本)9781665467636
The worlds of computing, communication, and storage have for a long time been treated separately, and even the recent trends of cloud computing, distributed computing, and mobile edge computing have not fundamentally changed the role of networks, still designed to move data between end users and pre-determined computation nodes, without true optimization of the end-to-end compute-communication process. However, the emergence of Metaverse applications, where users consume multimedia experiences that result from the real-time combination of distributed live sources and stored digital assets, has changed the requirements for, and possibilities of, systems that provide distributed caching, computation, and communication. We argue that the real-time interactive nature and high demands on data storage, streaming rates, and processing power of Metaverse applications will accelerate the merging of the cloud into the network, leading to highly-distributed tightly-integrated compute- and data-intensive networks becoming universal compute platforms for next-generation digital experiences. In this paper, we first describe the requirements of Metaverse applications and associated supporting infrastructure, including relevant use cases. We then outline a comprehensive cloud network flow mathematical framework, designed for the end-to-end optimization and control of such systems, and show numerical results illustrating its promising role for the efficient operation of Metaverse-ready networks.
Software Defined networking (SDN) is a new approach that has the potential to revolutionize the way we run network infrastructure. In order to provide a network with attack countermeasures, an Intrusion Detection Syst...
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ISBN:
(数字)9783031204364
ISBN:
(纸本)9783031204357;9783031204364
Software Defined networking (SDN) is a new approach that has the potential to revolutionize the way we run network infrastructure. In order to provide a network with attack countermeasures, an Intrusion Detection System (IDS) must be integrated into the SDN architecture. In this paper, we focus on IDS based on Machine Learning (ML) methods. The most problematic step in IDS evaluation is determining the appropriate dataset. Therefore, we propose a method that allows us to select the most appropriate dataset. In addition, the selection of an ML intrusion detection method related to an SDN architecture rather than another is another issue of this paper. We propose to integrate the severity of attacks into the standard metrics to differentiate between the quality of the results of ML methods. The severity of attacks will be computed using an adequate weighting of undetected intrusions (FN and FP) obtained in the testing phase.
The proposed framework addresses the critical need for secure multimedia data sharing by integrating image-to-audio encryption with advanced AI-based data-hiding techniques. The core concept involves transforming stat...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
The proposed framework addresses the critical need for secure multimedia data sharing by integrating image-to-audio encryption with advanced AI-based data-hiding techniques. The core concept involves transforming static visual content into dynamic auditory experiences, injecting semantic depth and artistic creativity into the resulting audio output. Unlike conventional methods, this approach goes beyond translating pixels into sound waves. It holds promise in multimedia communication, artistic expression, and data protection. To enhance security, this framework introduces AIenhanced data-hiding techniques, leveraging adversarial training and reinforcement learning. This extra layer ensures resistance to unauthorized access and tampering. In summary, this approach fuses image-to-audio encryption and AI-enhanced data hiding, aiming to revolutionize multimedia, thus offering a robust solution, transcending boundaries by safeguarding visual data through the immersive medium of audio.
Network analysis plays a significant role in business which is achieved through community detection. The relationship between the nodes is mined by community detection, which facilitates the analysis of complicated ne...
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Due to the advancement of technology, information security and user privacy have become a critical issue resulting in the use of various encryption techniques. Especially with the increasing use of social networking, ...
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The general accessibility of social networking sites like Facebook, Myspace and TikTok has transformed the way information is communicated, but it has also made it simpler to spread misleading information, thanks to c...
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ISBN:
(纸本)9798331522988
The general accessibility of social networking sites like Facebook, Myspace and TikTok has transformed the way information is communicated, but it has also made it simpler to spread misleading information, thanks to cutting-edge technologies like deepfakes. The use of artificial intelligence in deepfake technology produces material that is remarkably life- like but fake, making it difficult to identify and remove fraudulent content. The evolution of deepfake technology is reviewed in this study, starting with early breakthroughs like the Video Rewrite Program and continuing with more current developments like the Synthesizing Obama and Face2Face projects. We provide a comprehensive analysis of the state-of-the-art detection strategies, namely Region-based CNNs (RCNNs), Conventional Neural Networks (CNNs), and hybrid approaches that combine various deep learning techniques. Though these methods have advanced, there are still issues with accuracy, integration complexity, and adaptability to new deepfake techniques. This paper suggests a novel approach that integrates image, video, and audio analysis into a single detection framework in order to overcome these difficulties. The new approach is directed towards improvement of detection accuracy and the lowering of the false alarm using advanced and deep learning engines along with the real time processing requirements. Early results indicate that the detection capabilities have significantly improved compared to existing solutions operating in real time environment while having less latency and higher recall and precision rates. This method is more effective for deepfake detection and at the same time helps in augmenting the comprehensiveness and efficiency of the system there by making it suitable for wider uses. The study emphasizes the presence of the need for the evolution of static approaches towards the effective defection of sophisticated false information and the justification of new possibilities in the fight a
multimedia systems are the systems that are capable of processing multimedia applications and data. This is characterised by processing, generation, storage, rendition and manipulation of the multimedia information. A...
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